Tag: Analytics

One can store data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data …

Year after year, the field of ML is progressing at break-neck speed, and new algorithms and techniques are entering the space at a high frequency. Also, machine learning workloads are becoming increasingly more prevalent. However, there are significant challenges in democratizing machine learning and reliably scaling and deploying ML workloads.

In this article, we will have a look at some of the ML workload challenges and how data lakes can help overcome them.

Challenges In ML Workloads

Data Collection

ML workloads typically benefit from data — the more data is put into these workloads the better they become. So in order to make the most of the ML workloads, organisations across the world are looking for ways to collect data. However, the cost data collection and storage has to be low — one just cannot spend a huge amount of money collecting and storing data durably as one would not know when are where the data would be used.

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Extremely Experimental

ML workloads are iterative and experimental — it takes multiple experiments to check how the models are working. So, it is quite challenging. To over this ML workload challenge, a disposable infrastructure is something that organisations need. Why? Because this kind of infrastructure will allow training the ML model and when it’s no longer needed it can be disposed of.

Another thing that organisations working in the field of Machine Learning should keep in mind that they should be able to decouple compute and storage in order to run the workloads only when we need them.

Data Exploration

It is another challenge that organisations face. Collecting and storing huge amount of data is one thing, however, the struggle that organisations have to go through is exploring that data — what’s the format, what’s the schema, what data is usable, and what’s the data source.

It’s a whole different process and takes a lot of work. Talking about the exploration of data, schema on read is something that every organisation leverage. If you don’t know schema on read, it a data analysis strategy. In schema on read, data is applied to a plan or schema as it is pulled out of a stored location, rather than as it goes in. Another important thing to keep in mind is a data catalogue that centralizes all information on the data in one location.

Flexibility In Tool Set Selection

Selecting the set of tools is another challenge — tool sets differ based on the developer. Two different developers might not use the same kind of tool. So, it is important to have flexibility in selecting the correct set of tools. One should be able to quickly plug and play different tools and frameworks as there are a lot of new technologies are entering the space. Another thing is to keep data in the open data format as that it goes really well with most of the open source engines.

A Solution To All The Pain Points: Data Lake

A Data Lake is a central location in which to store all your data, regardless of its source or format. One can store data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.

Over the years, the concept of data lake has gained a lot of traction and now, in order to successfully generate business value from data and outperform peers, organisations across the world are actively working on building data lakes.

We have already mentioned the challenges that organisations face while working with ML workloads, and as to solve the pain points, building a data lake is a great option as it solves the issues.

Data Lakes let you import any amount of data that can come in real-time.

Data Lakes allow various roles in your organization like data scientists, data developers, and business analysts to access data with their choice of analytic tools and frameworks.

The ability to a data lake to harness more data, from different sources, in less time, is what makes it a better option when dealing with ML workloads. It not only empowers users to collaborate and analyze data in different ways but also helps in making decisions faster.

As the industry is realizing the growing importance of data, more and more data processing tools and next-generation technologies are coming out in the spotlight. Meanwhile, data analytics is also gaining much fame. Organizations, in order to thrive in the industry, need to adopt new data analytics innovations such as augmented analytics.

Recently, a market research company Frost and Sullivan discovered that augmented analytics being an advanced data processing tool is able to derive the real essence of insights from Big Data and push it towards Smart Data market summing up to $31.5 billion by 2022.

Basically, the tool automates the data-centric insights and facilitates with the refined information. This filtration of data at most saturated level is not possible using traditional analytics tools. Reportedly, Datameer, Xcalar, Incorta, and Bottlenose are already zooming into the development of end-to-end smart data analytics solutions. Such smart data solutions are designed to achieve valuable insights from Big Data.

Naga Avinash, Research Analyst, TechVision said – “Markets such as the US, the UK, India, and Dubai have rolled out several initiatives to use Artificial Intelligence (AI) and machine learning-powered data analytics tools to generate actionable insights from open data. Smart Data will help businesses reduce the risk of data loss and improve a range of activities such as operations, product development, predictive maintenance, customer experience, and innovation.”

It was depicted in the recent analysis brought to the table by Frost and Sullivan namely “Turning Big Data to Smart Data, that, Opportunities surfacing, key highlights of market developments and technologies are the key factors for converting big data into smart data, government-run programs and organizations employing data analytics. The report also shows the viewpoints of industry analyst and use cases on smart data.

Further, Naga Avinash added – “The evolution of advanced data analytics tools and self-service analytics endows business users instead of just data scientists with the ability to conduct analyses. Technology developers can ensure much wider adoption of their solutions by offering in-built security mechanisms that can block attackers in real time. They could also develop new business models such as shared data economy and even sell data-based products or utilities.”

Sellbyville, DE — (SBWIRE) — 03/16/2019 — Geospatial Imagery Analytics Market in Europe is expected to grow at a fast pace due to the availability of a highly advanced image collection and calibration infrastructure in the region. The technology is being extensively used in the engineering and construction industry in the region for ensuring safety at construction sites. Germany is expected to exhibit impressive growth over the forecast timeline due to the initiatives undertaken by institutions such as the German Bundeswehr Geo-information Centre and National Geospatial-Intelligence Agency to improve the accuracy and quality of image detection services in the country.

North America held a majority share of the geospatial imagery analytics market in 2017 and is expected to maintain a significant market share over the forecast timeline due to the extensive adoption of technologies such as IoT, AI, and cloud computing. The significant investment and resources invested by the U.S. government in the modernization of the GPS technology infrastructure in the country are also expected to contribute majorly to the regional market growth between 2018 and 2024.

Enterprises can develop targeted solutions for diverse business challenges that demand different responses for different geographic locations. Geospatial analytics can be used to combine imagery information acquired from different imaging platforms with the business data from finance, operations, and marketing departments to derive meaningful patterns and create graphs, maps, statistics, and cartograms to easily understand complex relationships between data sets. One of the popular use cases of geospatial imagery analytics market segmentation wherein; marketers can divide their customers into different groups with respect to common characteristics. The parameters used for dividing customers may include behavioral data, demographic data (gender or income), behavioral data (buying patterns), and lifestyle data. Such segmentation using geospatial analytics can help businesses to improve their promotional campaigns and customer retention initiatives.

Geospatial imagery analytics includes the gathering and processing of the imagery data acquired from GPS, satellite photography and Unmanned Aerial Vehicle (UAV) platforms, which are explicitly described in terms of geographic coordinates. The technology is being increasingly adopted in applications such as weather monitoring, crisis management, wildlife population management, and human population forecasting. For businesses, geospatial imagery analytics enables them to add the context of location and time to traditional data to observe the changes in different parameters over time. This helps them in identifying trends and patterns in a recognizable geographic context.

The key trends shaping the geospatial imagery analytics market landscape are the emergence and the rapid development of Big Data and Artificial Intelligence (AI) technologies. Recent advancements in computation and instrumentation have made the spatiotemporal data even bigger. This has introduced several constraints on traditional data analytics capabilities. As geospatial imagery analytics platforms generate huge datasets consistently, machine learning and deep learning approaches can be utilized to make the findings more accurate and meaningful. Using AI, the imagery data can be automatically compared to previous versions of the maps to effectively identify the changes in the associated parameters over time.

Global Market Insights, Inc., headquartered in Delaware, U.S., is a global market research and consulting service provider; offering syndicated and custom research reports along with growth consulting services. Our business intelligence and industry research reports offer clients with penetrative insights and actionable market data specially designed and presented to aid strategic decision making. These exhaustive reports are designed via a proprietary research methodology and are available for key industries such as chemicals, advanced materials, technology, renewable energy and biotechnology.

As per Current Market Revenue &Growth OnGlobal Life Science Analytics Market Observation Forecast to 2024

Global Life Science Analytics market report Market report consultations about the essential market development drivers and difficulties that the exporters and the market all in all face and gives a review of the key patterns rising in the market. It also talks about the market size of different segments and their growth aspects along with key leading countries, various stakeholders like investors,Research & media,Consultant,President,MD, CEOs, traders, suppliers and others. Life Science Analytics Market report covers the industry structure and even landscape, the problems along with business strategies and market effectiveness.

Topmost manufacturers/ Key player/ Economy by Business Leaders Leading Players of Life Science Analytics Market Are:

Sas Institute

IBM

Oracle

Quintiles

Accenture

Cognizant

Maxisit

Scio Health Analytics

Take Solutions

Wipro. And More……

Life Science Analytics is expected to grow at a CAGR of roughly xx% over the next five years, will reach xx million US$ in 2023, from xx million US$ in 2017, according to a new study.,

Major factors contributing to the growth of the global life science analytics market include technological advancements, availability of big data in the life science industry, growing adoption of analytics solutions for clinical trials.

Life Science Analytics Market Segment by Type covers:

Descriptive Analysis

Predictive Analysis

Prescriptive Analysis

Life Science Analytics Market Segment by Applications can be divided into:

Scope of the Life Science Analytics Market Report: This report focuses on the Life Science Analytics in global market, especially in North America, Europe and Asia-Pacific, South America, Middle East and Africa. This report categorizes the market based on manufacturers, regions, type and application., Asia is projected to be fastest-growing region in the market during the forecast period., The worldwide market for Life Science Analytics is expected to grow at a CAGR of roughly xx% over the next five years, will reach xx million US$ in 2023, from xx million US$ in 2017, according to a new GIR (Global Info Research) study.,

The Life Science Analytics Market Key player profile contains critical company information including:

Business description – A detailed description of the company’s operations and business divisions.

Corporate strategy – Analyst’s summarization of the company’s business strategy.

The next part also sheds light on the gap between supply and consumption. Apart from the mentioned information, growth rate of Life Science Analytics Industry in 2024 is also explained. Additionally, type wise and application wise consumption tables and figures of Life Science Analytics Industry are also given.